Pandas Profiling Report

Dataset statistics

Number of variables	23
Number of observations	1000
Missing cells	317
Missing cells (%)	1.4%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	179.8 KiB
Average record size in memory	184.1 B

Variable types

Categorical	9
Numeric	11
DateTime	1
Boolean	2

Warnings

`deceased_indicator` has constant value "False"	Constant
`country` has constant value "Australia"	Constant
`first_name` has a high cardinality: 940 distinct values	High cardinality
`last_name` has a high cardinality: 961 distinct values	High cardinality
`job_title` has a high cardinality: 184 distinct values	High cardinality
`address` has a high cardinality: 1000 distinct values	High cardinality
`Unnamed: 16` is highly correlated with `Unnamed: 17` and 2 other fields	High correlation
`Unnamed: 17` is highly correlated with `Unnamed: 16` and 2 other fields	High correlation
`Unnamed: 18` is highly correlated with `Unnamed: 16` and 2 other fields	High correlation
`Unnamed: 19` is highly correlated with `Unnamed: 16` and 2 other fields	High correlation
`Unnamed: 20` is highly correlated with `Rank` and 1 other fields	High correlation
`Rank` is highly correlated with `Unnamed: 20` and 1 other fields	High correlation
`Value` is highly correlated with `Unnamed: 20` and 1 other fields	High correlation
`postcode` is highly correlated with `property_valuation`	High correlation
`property_valuation` is highly correlated with `postcode`	High correlation
`Unnamed: 16` is highly correlated with `Unnamed: 17` and 2 other fields	High correlation
`Unnamed: 17` is highly correlated with `Unnamed: 16` and 2 other fields	High correlation
`Unnamed: 18` is highly correlated with `Unnamed: 16` and 2 other fields	High correlation
`Unnamed: 19` is highly correlated with `Unnamed: 16` and 2 other fields	High correlation
`Unnamed: 20` is highly correlated with `Rank` and 1 other fields	High correlation
`Rank` is highly correlated with `Unnamed: 20` and 1 other fields	High correlation
`Value` is highly correlated with `Unnamed: 20` and 1 other fields	High correlation
`Unnamed: 16` is highly correlated with `Unnamed: 17` and 2 other fields	High correlation
`Unnamed: 17` is highly correlated with `Unnamed: 16` and 2 other fields	High correlation
`Unnamed: 18` is highly correlated with `Unnamed: 16` and 2 other fields	High correlation
`Unnamed: 19` is highly correlated with `Unnamed: 16` and 2 other fields	High correlation
`Unnamed: 20` is highly correlated with `Rank` and 1 other fields	High correlation
`Rank` is highly correlated with `Unnamed: 20` and 1 other fields	High correlation
`Value` is highly correlated with `Unnamed: 20` and 1 other fields	High correlation
`Rank` is highly correlated with `Value` and 1 other fields	High correlation
`Unnamed: 17` is highly correlated with `Unnamed: 19` and 3 other fields	High correlation
`job_industry_category` is highly correlated with `gender`	High correlation
`Unnamed: 19` is highly correlated with `Unnamed: 17` and 2 other fields	High correlation
`gender` is highly correlated with `job_industry_category`	High correlation
`state` is highly correlated with `postcode`	High correlation
`Unnamed: 16` is highly correlated with `Unnamed: 17` and 2 other fields	High correlation
`Unnamed: 18` is highly correlated with `Unnamed: 17` and 2 other fields	High correlation
`Value` is highly correlated with `Rank` and 1 other fields	High correlation
`Unnamed: 20` is highly correlated with `Rank` and 1 other fields	High correlation
`property_valuation` is highly correlated with `postcode`	High correlation
`postcode` is highly correlated with `state` and 1 other fields	High correlation
`owns_car` is highly correlated with `Unnamed: 17`	High correlation
`state` is highly correlated with `country` and 1 other fields	High correlation
`country` is highly correlated with `state` and 5 other fields	High correlation
`deceased_indicator` is highly correlated with `state` and 5 other fields	High correlation
`wealth_segment` is highly correlated with `country` and 1 other fields	High correlation
`job_industry_category` is highly correlated with `country` and 1 other fields	High correlation
`gender` is highly correlated with `country` and 1 other fields	High correlation
`owns_car` is highly correlated with `country` and 1 other fields	High correlation
`last_name` has 29 (2.9%) missing values	Missing
`DOB` has 17 (1.7%) missing values	Missing
`job_title` has 106 (10.6%) missing values	Missing
`job_industry_category` has 165 (16.5%) missing values	Missing
`first_name` is uniformly distributed	Uniform
`last_name` is uniformly distributed	Uniform
`address` is uniformly distributed	Uniform
`address` has unique values	Unique

Reproduction

Analysis started	2021-07-28 19:48:48.320285
Analysis finished	2021-07-28 19:49:14.444215
Duration	26.12 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

first_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	940
Distinct (%)	94.0%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Dorian	3
Rozamond	3
Mandie	3
Tyne	2
Harman	2
Other values (935)	987

Length

Max length	13
Median length	6
Mean length	6.087
Min length	2

Characters and Unicode

Total characters	6087
Distinct characters	52
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	883 ?
Unique (%)	88.3%

Sample

1st row	Chickie
2nd row	Morly
3rd row	Ardelis
4th row	Lucine
5th row	Melinda

Common Values

Value	Count	Frequency (%)
Dorian	3	0.3%
Rozamond	3	0.3%
Mandie	3	0.3%
Tyne	2	0.2%
Harman	2	0.2%
Bartram	2	0.2%
Muffin	2	0.2%
Jesse	2	0.2%
Liane	2	0.2%
Nicol	2	0.2%
Other values (930)	977	97.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
mandie	3	0.3%
rozamond	3	0.3%
dorian	3	0.3%
wheeler	2	0.2%
geoff	2	0.2%
bartram	2	0.2%
art	2	0.2%
beverlee	2	0.2%
suzy	2	0.2%
laurie	2	0.2%
Other values (930)	977	97.7%

Most occurring characters

Value	Count	Frequency (%)
e	702	11.5%
a	631	10.4%
i	566	9.3%
n	488	8.0%
r	434	7.1%
l	414	6.8%
o	303	5.0%
t	223	3.7%
d	193	3.2%
y	168	2.8%
Other values (42)	1965	32.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5086	83.6%
Uppercase Letter	1000	16.4%
Dash Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	702	13.8%
a	631	12.4%
i	566	11.1%
n	488	9.6%
r	434	8.5%
l	414	8.1%
o	303	6.0%
t	223	4.4%
d	193	3.8%
y	168	3.3%
Other values (16)	964	19.0%

Uppercase Letter

Value	Count	Frequency (%)
A	88	8.8%
C	80	8.0%
M	75	7.5%
D	71	7.1%
S	64	6.4%
R	63	6.3%
L	61	6.1%
B	56	5.6%
K	54	5.4%
G	54	5.4%
Other values (15)	334	33.4%

Dash Punctuation

Value	Count	Frequency (%)
-	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6086	> 99.9%
Common	1	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	702	11.5%
a	631	10.4%
i	566	9.3%
n	488	8.0%
r	434	7.1%
l	414	6.8%
o	303	5.0%
t	223	3.7%
d	193	3.2%
y	168	2.8%
Other values (41)	1964	32.3%

Common

Value	Count	Frequency (%)
-	1	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6087	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	702	11.5%
a	631	10.4%
i	566	9.3%
n	488	8.0%
r	434	7.1%
l	414	6.8%
o	303	5.0%
t	223	3.7%
d	193	3.2%
y	168	2.8%
Other values (42)	1965	32.3%

last_name
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	961
Distinct (%)	99.0%
Missing	29
Missing (%)	2.9%
Memory size	7.9 KiB

Eade	2
Burgoine	2
Borsi	2
Crellim	2
Van den Velde	2
Other values (956)	961

Length

Max length	21
Median length	7
Mean length	7.026776519
Min length	3

Characters and Unicode

Total characters	6823
Distinct characters	54
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	951 ?
Unique (%)	97.9%

Sample

1st row	Brister
2nd row	Genery
3rd row	Forrester
4th row	Stutt
5th row	Hadlee

Common Values

Value	Count	Frequency (%)
Eade	2	0.2%
Burgoine	2	0.2%
Borsi	2	0.2%
Crellim	2	0.2%
Van den Velde	2	0.2%
Sissel	2	0.2%
Sturch	2	0.2%
Minshall	2	0.2%
Shoesmith	2	0.2%
Hallt	2	0.2%
Other values (951)	951	95.1%
(Missing)	29	2.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
van	3	0.3%
de	3	0.3%
den	3	0.3%
velde	2	0.2%
shoesmith	2	0.2%
crellim	2	0.2%
borsi	2	0.2%
burgoine	2	0.2%
eade	2	0.2%
hallt	2	0.2%
Other values (960)	963	97.7%

Most occurring characters

Value	Count	Frequency (%)
e	707	10.4%
a	529	7.8%
n	500	7.3%
r	454	6.7%
o	441	6.5%
l	410	6.0%
i	406	6.0%
t	361	5.3%
s	316	4.6%
d	207	3.0%
Other values (44)	2492	36.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5783	84.8%
Uppercase Letter	1014	14.9%
Space Separator	15	0.2%
Other Punctuation	10	0.1%
Dash Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	707	12.2%
a	529	9.1%
n	500	8.6%
r	454	7.9%
o	441	7.6%
l	410	7.1%
i	406	7.0%
t	361	6.2%
s	316	5.5%
d	207	3.6%
Other values (16)	1452	25.1%

Uppercase Letter

Value	Count	Frequency (%)
B	120	11.8%
S	94	9.3%
C	93	9.2%
M	79	7.8%
D	67	6.6%
P	58	5.7%
H	56	5.5%
A	51	5.0%
G	48	4.7%
R	43	4.2%
Other values (15)	305	30.1%

Space Separator

Value	Count	Frequency (%)
	15	100.0%

Other Punctuation

Value	Count	Frequency (%)
'	10	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6797	99.6%
Common	26	0.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	707	10.4%
a	529	7.8%
n	500	7.4%
r	454	6.7%
o	441	6.5%
l	410	6.0%
i	406	6.0%
t	361	5.3%
s	316	4.6%
d	207	3.0%
Other values (41)	2466	36.3%

Common

Value	Count	Frequency (%)
	15	57.7%
'	10	38.5%
-	1	3.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6823	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	707	10.4%
a	529	7.8%
n	500	7.3%
r	454	6.7%
o	441	6.5%
l	410	6.0%
i	406	6.0%
t	361	5.3%
s	316	4.6%
d	207	3.0%
Other values (44)	2492	36.5%

gender
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Female	513
Male	470
U	17

Length

Max length	6
Median length	6
Mean length	4.975
Min length	1

Characters and Unicode

Total characters	4975
Distinct characters	7
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Male
2nd row	Male
3rd row	Female
4th row	Female
5th row	Female

Common Values

Value	Count	Frequency (%)
Female	513	51.3%
Male	470	47.0%
U	17	1.7%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
female	513	51.3%
male	470	47.0%
u	17	1.7%

Most occurring characters

Value	Count	Frequency (%)
e	1496	30.1%
a	983	19.8%
l	983	19.8%
F	513	10.3%
m	513	10.3%
M	470	9.4%
U	17	0.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	3975	79.9%
Uppercase Letter	1000	20.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1496	37.6%
a	983	24.7%
l	983	24.7%
m	513	12.9%

Uppercase Letter

Value	Count	Frequency (%)
F	513	51.3%
M	470	47.0%
U	17	1.7%

Most occurring scripts

Value	Count	Frequency (%)
Latin	4975	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1496	30.1%
a	983	19.8%
l	983	19.8%
F	513	10.3%
m	513	10.3%
M	470	9.4%
U	17	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4975	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1496	30.1%
a	983	19.8%
l	983	19.8%
F	513	10.3%
m	513	10.3%
M	470	9.4%
U	17	0.3%

past_3_years_bike_related_purchases
Real number (ℝ_≥0)

Distinct	100
Distinct (%)	10.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	49.836

Minimum	0
Maximum	99
Zeros	9
Zeros (%)	0.9%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0
5-th percentile	5
Q1	26.75
median	51
Q3	72
95-th percentile	94
Maximum	99
Range	99
Interquartile range (IQR)	45.25

Descriptive statistics

Standard deviation	27.79668613
Coefficient of variation (CV)	0.5577631858
Kurtosis	-1.088048884
Mean	49.836
Median Absolute Deviation (MAD)	22.5
Skewness	-0.06562186172
Sum	49836
Variance	772.6557598
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
60	20	2.0%
59	18	1.8%
70	17	1.7%
42	17	1.7%
37	16	1.6%
11	16	1.6%
47	15	1.5%
84	14	1.4%
62	14	1.4%
57	14	1.4%
Other values (90)	839	83.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	9	0.9%
1	8	0.8%
2	9	0.9%
3	9	0.9%
4	10	1.0%
5	13	1.3%
6	10	1.0%
7	13	1.3%
8	7	0.7%
9	5	0.5%

Value	Count	Frequency (%)
99	9	0.9%
98	6	0.6%
97	11	1.1%
96	9	0.9%
95	8	0.8%
94	12	1.2%
93	9	0.9%
92	5	0.5%
91	8	0.8%
90	6	0.6%

DOB
Date

MISSING

Distinct	958
Distinct (%)	97.5%
Missing	17
Missing (%)	1.7%
Memory size	7.9 KiB

Minimum	1938-06-08 00:00:00
Maximum	2002-02-27 00:00:00

Histogram

Histogram with fixed size bins (bins=50)

job_title
Categorical

HIGH CARDINALITY
MISSING

Distinct	184
Distinct (%)	20.6%
Missing	106
Missing (%)	10.6%
Memory size	7.9 KiB

Associate Professor	15
Environmental Tech	14
Software Consultant	14
Chief Design Engineer	13
Assistant Manager	12
Other values (179)	826

Length

Max length	36
Median length	18
Mean length	18.08836689
Min length	5

Characters and Unicode

Total characters	16171
Distinct characters	47
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	45 ?
Unique (%)	5.0%

Sample

1st row	General Manager
2nd row	Structural Engineer
3rd row	Senior Cost Accountant
4th row	Account Representative III
5th row	Financial Analyst

Common Values

Value	Count	Frequency (%)
Associate Professor	15	1.5%
Environmental Tech	14	1.4%
Software Consultant	14	1.4%
Chief Design Engineer	13	1.3%
Assistant Manager	12	1.2%
VP Sales	12	1.2%
Assistant Media Planner	12	1.2%
Senior Sales Associate	12	1.2%
Cost Accountant	12	1.2%
Payment Adjustment Coordinator	11	1.1%
Other values (174)	767	76.7%
(Missing)	106	10.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
engineer	131	6.3%
assistant	82	3.9%
manager	76	3.7%
analyst	66	3.2%
iv	52	2.5%
iii	50	2.4%
vp	46	2.2%
ii	44	2.1%
senior	44	2.1%
sales	44	2.1%
Other values (117)	1444	69.5%

Most occurring characters

Value	Count	Frequency (%)
e	1578	9.8%
n	1279	7.9%
a	1253	7.7%
t	1218	7.5%
	1185	7.3%
i	1124	7.0%
r	1083	6.7%
s	966	6.0%
o	773	4.8%
c	678	4.2%
Other values (37)	5034	31.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	12650	78.2%
Uppercase Letter	2328	14.4%
Space Separator	1185	7.3%
Other Punctuation	8	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1578	12.5%
n	1279	10.1%
a	1253	9.9%
t	1218	9.6%
i	1124	8.9%
r	1083	8.6%
s	966	7.6%
o	773	6.1%
c	678	5.4%
l	532	4.2%
Other values (14)	2166	17.1%

Uppercase Letter

Value	Count	Frequency (%)
I	351	15.1%
A	346	14.9%
S	271	11.6%
E	201	8.6%
P	197	8.5%
C	156	6.7%
M	134	5.8%
D	121	5.2%
V	98	4.2%
T	78	3.4%
Other values (11)	375	16.1%

Space Separator

Value	Count	Frequency (%)
	1185	100.0%

Other Punctuation

Value	Count	Frequency (%)
/	8	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	14978	92.6%
Common	1193	7.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1578	10.5%
n	1279	8.5%
a	1253	8.4%
t	1218	8.1%
i	1124	7.5%
r	1083	7.2%
s	966	6.4%
o	773	5.2%
c	678	4.5%
l	532	3.6%
Other values (35)	4494	30.0%

Common

Value	Count	Frequency (%)
	1185	99.3%
/	8	0.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	16171	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1578	9.8%
n	1279	7.9%
a	1253	7.7%
t	1218	7.5%
	1185	7.3%
i	1124	7.0%
r	1083	6.7%
s	966	6.0%
o	773	4.8%
c	678	4.2%
Other values (37)	5034	31.1%

job_industry_category
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	9
Distinct (%)	1.1%
Missing	165
Missing (%)	16.5%
Memory size	7.9 KiB

Financial Services	203
Manufacturing	199
Health	152
Retail	78
Property	64
Other values (4)	139

Length

Max length	18
Median length	13
Mean length	11.31976048
Min length	2

Characters and Unicode

Total characters	9452
Distinct characters	29
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Manufacturing
2nd row	Property
3rd row	Financial Services
4th row	Manufacturing
5th row	Financial Services

Common Values

Value	Count	Frequency (%)
Financial Services	203	20.3%
Manufacturing	199	19.9%
Health	152	15.2%
Retail	78	7.8%
Property	64	6.4%
IT	51	5.1%
Entertainment	37	3.7%
Argiculture	26	2.6%
Telecommunications	25	2.5%
(Missing)	165	16.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
financial	203	19.6%
services	203	19.6%
manufacturing	199	19.2%
health	152	14.6%
retail	78	7.5%
property	64	6.2%
it	51	4.9%
entertainment	37	3.6%
argiculture	26	2.5%
telecommunications	25	2.4%

Most occurring characters

Value	Count	Frequency (%)
a	1096	11.6%
i	999	10.6%
n	965	10.2%
e	850	9.0%
c	681	7.2%
t	655	6.9%
r	619	6.5%
l	484	5.1%
u	475	5.0%
s	228	2.4%
Other values (19)	2400	25.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	8160	86.3%
Uppercase Letter	1089	11.5%
Space Separator	203	2.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	1096	13.4%
i	999	12.2%
n	965	11.8%
e	850	10.4%
c	681	8.3%
t	655	8.0%
r	619	7.6%
l	484	5.9%
u	475	5.8%
s	228	2.8%
Other values (8)	1108	13.6%

Uppercase Letter

Value	Count	Frequency (%)
F	203	18.6%
S	203	18.6%
M	199	18.3%
H	152	14.0%
R	78	7.2%
T	76	7.0%
P	64	5.9%
I	51	4.7%
E	37	3.4%
A	26	2.4%

Space Separator

Value	Count	Frequency (%)
	203	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9249	97.9%
Common	203	2.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	1096	11.8%
i	999	10.8%
n	965	10.4%
e	850	9.2%
c	681	7.4%
t	655	7.1%
r	619	6.7%
l	484	5.2%
u	475	5.1%
s	228	2.5%
Other values (18)	2197	23.8%

Common

Value	Count	Frequency (%)
	203	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9452	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	1096	11.6%
i	999	10.6%
n	965	10.2%
e	850	9.0%
c	681	7.2%
t	655	6.9%
r	619	6.5%
l	484	5.1%
u	475	5.0%
s	228	2.4%
Other values (19)	2400	25.4%

wealth_segment
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Mass Customer	508
High Net Worth	251
Affluent Customer	241

Length

Max length	17
Median length	13
Mean length	14.215
Min length	13

Characters and Unicode

Total characters	14215
Distinct characters	21
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Mass Customer
2nd row	Mass Customer
3rd row	Affluent Customer
4th row	Affluent Customer
5th row	Affluent Customer

Common Values

Value	Count	Frequency (%)
Mass Customer	508	50.8%
High Net Worth	251	25.1%
Affluent Customer	241	24.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
customer	749	33.3%
mass	508	22.6%
net	251	11.2%
worth	251	11.2%
high	251	11.2%
affluent	241	10.7%

Most occurring characters

Value	Count	Frequency (%)
s	1765	12.4%
t	1492	10.5%
	1251	8.8%
e	1241	8.7%
o	1000	7.0%
r	1000	7.0%
u	990	7.0%
C	749	5.3%
m	749	5.3%
M	508	3.6%
Other values (11)	3470	24.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	10713	75.4%
Uppercase Letter	2251	15.8%
Space Separator	1251	8.8%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
s	1765	16.5%
t	1492	13.9%
e	1241	11.6%
o	1000	9.3%
r	1000	9.3%
u	990	9.2%
m	749	7.0%
a	508	4.7%
h	502	4.7%
f	482	4.5%
Other values (4)	984	9.2%

Uppercase Letter

Value	Count	Frequency (%)
C	749	33.3%
M	508	22.6%
H	251	11.2%
N	251	11.2%
W	251	11.2%
A	241	10.7%

Space Separator

Value	Count	Frequency (%)
	1251	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	12964	91.2%
Common	1251	8.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
s	1765	13.6%
t	1492	11.5%
e	1241	9.6%
o	1000	7.7%
r	1000	7.7%
u	990	7.6%
C	749	5.8%
m	749	5.8%
M	508	3.9%
a	508	3.9%
Other values (10)	2962	22.8%

Common

Value	Count	Frequency (%)
	1251	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	14215	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
s	1765	12.4%
t	1492	10.5%
	1251	8.8%
e	1241	8.7%
o	1000	7.0%
r	1000	7.0%
u	990	7.0%
C	749	5.3%
m	749	5.3%
M	508	3.6%
Other values (11)	3470	24.4%

deceased_indicator
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	1.1 KiB

False	1000

Common Values
Chart

Value	Count	Frequency (%)
False	1000	100.0%

owns_car
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	1.1 KiB

False	507
True	493

Common Values
Chart

Value	Count	Frequency (%)
False	507	50.7%
True	493	49.3%

tenure
Real number (ℝ_≥0)

Distinct	23
Distinct (%)	2.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	11.388

Minimum	0
Maximum	22
Zeros	2
Zeros (%)	0.2%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0
5-th percentile	3
Q1	7
median	11
Q3	15
95-th percentile	20
Maximum	22
Range	22
Interquartile range (IQR)	8

Descriptive statistics

Standard deviation	5.037144908
Coefficient of variation (CV)	0.442320417
Kurtosis	-0.8128152156
Mean	11.388
Median Absolute Deviation (MAD)	4
Skewness	0.07089079797
Sum	11388
Variance	25.37282883
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=23)

Value	Count	Frequency (%)
9	79	7.9%
13	74	7.4%
11	68	6.8%
10	63	6.3%
12	61	6.1%
7	60	6.0%
5	60	6.0%
17	59	5.9%
15	58	5.8%
8	55	5.5%
Other values (13)	363	36.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	2	0.2%
1	8	0.8%
2	15	1.5%
3	26	2.6%
4	36	3.6%
5	60	6.0%
6	45	4.5%
7	60	6.0%
8	55	5.5%
9	79	7.9%

Value	Count	Frequency (%)
22	12	1.2%
21	24	2.4%
20	22	2.2%
19	34	3.4%
18	36	3.6%
17	59	5.9%
16	49	4.9%
15	58	5.8%
14	54	5.4%
13	74	7.4%

address
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	1000
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

309 Maple Wood Pass	1
5880 Hauk Street	1
602 Meadow Vale Lane	1
9 Killdeer Circle	1
7 Brentwood Circle	1
Other values (995)	995

Length

Max length	26
Median length	18
Mean length	17.582
Min length	9

Characters and Unicode

Total characters	17582
Distinct characters	60
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1000 ?
Unique (%)	100.0%

Sample

1st row	45 Shopko Center
2nd row	14 Mccormick Park
3rd row	5 Colorado Crossing
4th row	207 Annamark Plaza
5th row	115 Montana Place

Common Values

Value	Count	Frequency (%)
309 Maple Wood Pass	1	0.1%
5880 Hauk Street	1	0.1%
602 Meadow Vale Lane	1	0.1%
9 Killdeer Circle	1	0.1%
7 Brentwood Circle	1	0.1%
6115 Forest Crossing	1	0.1%
05475 Elgar Place	1	0.1%
33 Pond Point	1	0.1%
2 Main Lane	1	0.1%
7 Messerschmidt Crossing	1	0.1%
Other values (990)	990	99.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
park	59	1.9%
crossing	59	1.9%
center	58	1.9%
avenue	55	1.8%
street	55	1.8%
lane	55	1.8%
point	54	1.7%
hill	51	1.6%
plaza	50	1.6%
court	49	1.6%
Other values (1137)	2563	82.5%

Most occurring characters

Value	Count	Frequency (%)
	2108	12.0%
e	1394	7.9%
a	1152	6.6%
r	1072	6.1%
n	884	5.0%
o	787	4.5%
i	748	4.3%
l	732	4.2%
t	645	3.7%
s	469	2.7%
Other values (50)	7591	43.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	10401	59.2%
Decimal Number	2975	16.9%
Space Separator	2108	12.0%
Uppercase Letter	2098	11.9%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1394	13.4%
a	1152	11.1%
r	1072	10.3%
n	884	8.5%
o	787	7.6%
i	748	7.2%
l	732	7.0%
t	645	6.2%
s	469	4.5%
d	318	3.1%
Other values (16)	2200	21.2%

Uppercase Letter

Value	Count	Frequency (%)
P	337	16.1%
C	272	13.0%
S	167	8.0%
M	142	6.8%
A	142	6.8%
T	114	5.4%
R	113	5.4%
D	112	5.3%
L	108	5.1%
H	99	4.7%
Other values (13)	492	23.5%

Decimal Number

Value	Count	Frequency (%)
6	330	11.1%
7	317	10.7%
0	315	10.6%
2	305	10.3%
3	300	10.1%
1	294	9.9%
5	293	9.8%
9	279	9.4%
8	273	9.2%
4	269	9.0%

Space Separator

Value	Count	Frequency (%)
	2108	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	12499	71.1%
Common	5083	28.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1394	11.2%
a	1152	9.2%
r	1072	8.6%
n	884	7.1%
o	787	6.3%
i	748	6.0%
l	732	5.9%
t	645	5.2%
s	469	3.8%
P	337	2.7%
Other values (39)	4279	34.2%

Common

Value	Count	Frequency (%)
	2108	41.5%
6	330	6.5%
7	317	6.2%
0	315	6.2%
2	305	6.0%
3	300	5.9%
1	294	5.8%
5	293	5.8%
9	279	5.5%
8	273	5.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	17582	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	2108	12.0%
e	1394	7.9%
a	1152	6.6%
r	1072	6.1%
n	884	5.0%
o	787	4.5%
i	748	4.3%
l	732	4.2%
t	645	3.7%
s	469	2.7%
Other values (50)	7591	43.2%

postcode
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	522
Distinct (%)	52.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3019.227

Minimum	2000
Maximum	4879
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	2000
5-th percentile	2046
Q1	2209
median	2800
Q3	3845.5
95-th percentile	4508.05
Maximum	4879
Range	2879
Interquartile range (IQR)	1636.5

Descriptive statistics

Standard deviation	848.8957672
Coefficient of variation (CV)	0.2811632803
Kurtosis	-1.142498217
Mean	3019.227
Median Absolute Deviation (MAD)	635.5
Skewness	0.4921079268
Sum	3019227
Variance	720624.0235
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2232	9	0.9%
2145	9	0.9%
2168	7	0.7%
2750	7	0.7%
3029	7	0.7%
4207	7	0.7%
2148	7	0.7%
3977	7	0.7%
2066	6	0.6%
4350	6	0.6%
Other values (512)	928	92.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2000	1	0.1%
2007	3	0.3%
2009	2	0.2%
2010	4	0.4%
2011	4	0.4%
2015	1	0.1%
2016	2	0.2%
2017	1	0.1%
2019	3	0.3%
2022	1	0.1%

Value	Count	Frequency (%)
4879	1	0.1%
4852	1	0.1%
4818	2	0.2%
4817	2	0.2%
4814	3	0.3%
4744	1	0.1%
4740	2	0.2%
4720	1	0.1%
4717	1	0.1%
4710	1	0.1%

state
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

NSW	506
VIC	266
QLD	228

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3000
Distinct characters	9
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	QLD
2nd row	NSW
3rd row	VIC
4th row	QLD
5th row	NSW

Common Values

Value	Count	Frequency (%)
NSW	506	50.6%
VIC	266	26.6%
QLD	228	22.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
nsw	506	50.6%
vic	266	26.6%
qld	228	22.8%

Most occurring characters

Value	Count	Frequency (%)
N	506	16.9%
S	506	16.9%
W	506	16.9%
V	266	8.9%
I	266	8.9%
C	266	8.9%
Q	228	7.6%
L	228	7.6%
D	228	7.6%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	3000	100.0%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
N	506	16.9%
S	506	16.9%
W	506	16.9%
V	266	8.9%
I	266	8.9%
C	266	8.9%
Q	228	7.6%
L	228	7.6%
D	228	7.6%

Most occurring scripts

Value	Count	Frequency (%)
Latin	3000	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
N	506	16.9%
S	506	16.9%
W	506	16.9%
V	266	8.9%
I	266	8.9%
C	266	8.9%
Q	228	7.6%
L	228	7.6%
D	228	7.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3000	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
N	506	16.9%
S	506	16.9%
W	506	16.9%
V	266	8.9%
I	266	8.9%
C	266	8.9%
Q	228	7.6%
L	228	7.6%
D	228	7.6%

country
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	7.9 KiB

Australia	1000

Length

Max length	9
Median length	9
Mean length	9
Min length	9

Characters and Unicode

Total characters	9000
Distinct characters	8
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Australia
2nd row	Australia
3rd row	Australia
4th row	Australia
5th row	Australia

Common Values

Value	Count	Frequency (%)
Australia	1000	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
australia	1000	100.0%

Most occurring characters

Value	Count	Frequency (%)
a	2000	22.2%
A	1000	11.1%
u	1000	11.1%
s	1000	11.1%
t	1000	11.1%
r	1000	11.1%
l	1000	11.1%
i	1000	11.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	8000	88.9%
Uppercase Letter	1000	11.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	2000	25.0%
u	1000	12.5%
s	1000	12.5%
t	1000	12.5%
r	1000	12.5%
l	1000	12.5%
i	1000	12.5%

Uppercase Letter

Value	Count	Frequency (%)
A	1000	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9000	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	2000	22.2%
A	1000	11.1%
u	1000	11.1%
s	1000	11.1%
t	1000	11.1%
r	1000	11.1%
l	1000	11.1%
i	1000	11.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9000	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	2000	22.2%
A	1000	11.1%
u	1000	11.1%
s	1000	11.1%
t	1000	11.1%
r	1000	11.1%
l	1000	11.1%
i	1000	11.1%

property_valuation
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	12
Distinct (%)	1.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.397

Minimum	1
Maximum	12
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	6
median	8
Q3	9
95-th percentile	11
Maximum	12
Range	11
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.758804452
Coefficient of variation (CV)	0.3729626134
Kurtosis	-0.3712799928
Mean	7.397
Median Absolute Deviation (MAD)	2
Skewness	-0.5576112079
Sum	7397
Variance	7.611002002
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
9	173	17.3%
8	162	16.2%
7	138	13.8%
10	116	11.6%
6	70	7.0%
11	62	6.2%
5	57	5.7%
4	53	5.3%
3	51	5.1%
12	46	4.6%
Other values (2)	72	7.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	30	3.0%
2	42	4.2%
3	51	5.1%
4	53	5.3%
5	57	5.7%
6	70	7.0%
7	138	13.8%
8	162	16.2%
9	173	17.3%
10	116	11.6%

Value	Count	Frequency (%)
12	46	4.6%
11	62	6.2%
10	116	11.6%
9	173	17.3%
8	162	16.2%
7	138	13.8%
6	70	7.0%
5	57	5.7%
4	53	5.3%
3	51	5.1%

Unnamed: 16
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	71
Distinct (%)	7.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.74734

Minimum	0.4
Maximum	1.1
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0.4
5-th percentile	0.43
Q1	0.57
median	0.75
Q3	0.92
95-th percentile	1.07
Maximum	1.1
Range	0.7
Interquartile range (IQR)	0.35

Descriptive statistics

Standard deviation	0.2050823815
Coefficient of variation (CV)	0.2744164389
Kurtosis	-1.194441209
Mean	0.74734
Median Absolute Deviation (MAD)	0.17
Skewness	0.04085608504
Sum	747.34
Variance	0.04205878318
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0.62	26	2.6%
0.86	24	2.4%
0.58	20	2.0%
0.49	20	2.0%
1.08	20	2.0%
0.77	20	2.0%
0.84	19	1.9%
1.07	19	1.9%
0.56	19	1.9%
0.57	18	1.8%
Other values (61)	795	79.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.4	15	1.5%
0.41	16	1.6%
0.42	10	1.0%
0.43	11	1.1%
0.44	17	1.7%
0.45	9	0.9%
0.46	14	1.4%
0.47	15	1.5%
0.48	17	1.7%
0.49	20	2.0%

Value	Count	Frequency (%)
1.1	13	1.3%
1.09	14	1.4%
1.08	20	2.0%
1.07	19	1.9%
1.06	11	1.1%
1.05	17	1.7%
1.04	14	1.4%
1.03	11	1.1%
1.02	12	1.2%
1.01	13	1.3%

Unnamed: 17
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	132
Distinct (%)	13.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.839005

Minimum	0.4
Maximum	1.375
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0.4
5-th percentile	0.47
Q1	0.6375
median	0.82
Q3	1.031875
95-th percentile	1.3
Maximum	1.375
Range	0.975
Interquartile range (IQR)	0.394375

Descriptive statistics

Standard deviation	0.2488584497
Coefficient of variation (CV)	0.2966114025
Kurtosis	-0.8040909217
Mean	0.839005
Median Absolute Deviation (MAD)	0.2
Skewness	0.2688519805
Sum	839.005
Variance	0.061930528
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1.05	20	2.0%
0.55	19	1.9%
0.775	15	1.5%
0.95	15	1.5%
0.5	14	1.4%
1.07	14	1.4%
0.6	14	1.4%
0.58	13	1.3%
0.7	13	1.3%
0.86	13	1.3%
Other values (122)	850	85.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.4	6	0.6%
0.41	11	1.1%
0.42	4	0.4%
0.43	6	0.6%
0.44	6	0.6%
0.45	4	0.4%
0.46	7	0.7%
0.47	7	0.7%
0.48	11	1.1%
0.49	11	1.1%

Value	Count	Frequency (%)
1.375	7	0.7%
1.3625	9	0.9%
1.35	12	1.2%
1.3375	5	0.5%
1.325	5	0.5%
1.3125	8	0.8%
1.3	7	0.7%
1.2875	6	0.6%
1.275	5	0.5%
1.2625	8	0.8%

Unnamed: 18
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	183
Distinct (%)	18.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.9426725

Minimum	0.4
Maximum	1.71875
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0.4
5-th percentile	0.5125
Q1	0.7125
median	0.9125
Q3	1.14296875
95-th percentile	1.453125
Maximum	1.71875
Range	1.31875
Interquartile range (IQR)	0.43046875

Descriptive statistics

Standard deviation	0.2948324662
Coefficient of variation (CV)	0.3127623498
Kurtosis	-0.4868813031
Mean	0.9426725
Median Absolute Deviation (MAD)	0.2125
Skewness	0.4057067456
Sum	942.6725
Variance	0.08692618315
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0.75	18	1.8%
1.05	15	1.5%
0.9625	15	1.5%
1.35	14	1.4%
1.3125	14	1.4%
1.1875	14	1.4%
0.8125	13	1.3%
0.55	13	1.3%
1.1375	13	1.3%
0.725	13	1.3%
Other values (173)	858	85.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.4	4	0.4%
0.41	6	0.6%
0.42	1	0.1%
0.43	4	0.4%
0.44	3	0.3%
0.45	1	0.1%
0.46	3	0.3%
0.47	3	0.3%
0.48	4	0.4%
0.49	7	0.7%

Value	Count	Frequency (%)
1.71875	4	0.4%
1.703125	3	0.3%
1.6875	4	0.4%
1.671875	2	0.2%
1.65625	1	0.1%
1.640625	3	0.3%
1.625	3	0.3%
1.609375	5	0.5%
1.59375	3	0.3%
1.578125	4	0.4%

Unnamed: 19
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	321
Distinct (%)	32.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.87051425

Minimum	0.34
Maximum	1.71875
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0.34
5-th percentile	0.4675
Q1	0.65875
median	0.842625
Q3	1.0625
95-th percentile	1.36796875
Maximum	1.71875
Range	1.37875
Interquartile range (IQR)	0.40375

Descriptive statistics

Standard deviation	0.2808905394
Coefficient of variation (CV)	0.3226719601
Kurtosis	-0.3139375306
Mean	0.87051425
Median Absolute Deviation (MAD)	0.205125
Skewness	0.4723827623
Sum	870.51425
Variance	0.0788994951
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1.0625	14	1.4%
0.75	11	1.1%
0.6375	10	1.0%
1.05	9	0.9%
0.8125	9	0.9%
1.009375	9	0.9%
0.68	9	0.9%
1.375	9	0.9%
1.1375	9	0.9%
1.3125	9	0.9%
Other values (311)	902	90.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.34	1	0.1%
0.3485	4	0.4%
0.357	1	0.1%
0.374	1	0.1%
0.3825	1	0.1%
0.391	1	0.1%
0.3995	2	0.2%
0.4	3	0.3%
0.408	2	0.2%
0.41	2	0.2%

Value	Count	Frequency (%)
1.71875	1	0.1%
1.703125	1	0.1%
1.6875	2	0.2%
1.671875	1	0.1%
1.640625	2	0.2%
1.625	2	0.2%
1.609375	1	0.1%
1.59375	2	0.2%
1.578125	2	0.2%
1.5625	1	0.1%

Unnamed: 20
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	324
Distinct (%)	32.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	498.819

Minimum	1
Maximum	1000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	1
5-th percentile	50
Q1	250
median	500
Q3	750.25
95-th percentile	948.15
Maximum	1000
Range	999
Interquartile range (IQR)	500.25

Descriptive statistics

Standard deviation	288.8109971
Coefficient of variation (CV)	0.5789895675
Kurtosis	-1.200749808
Mean	498.819
Median Absolute Deviation (MAD)	250
Skewness	0.001245859611
Sum	498819
Variance	83411.79203
Monotonicity	Increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
760	13	1.3%
259	12	1.2%
455	9	0.9%
904	9	0.9%
386	9	0.9%
133	9	0.9%
700	8	0.8%
820	8	0.8%
312	8	0.8%
536	8	0.8%
Other values (314)	907	90.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	3	0.3%
4	2	0.2%
6	2	0.2%
8	2	0.2%
10	2	0.2%
12	1	0.1%
13	1	0.1%
14	2	0.2%
16	1	0.1%
17	2	0.2%

Value	Count	Frequency (%)
1000	1	0.1%
997	3	0.3%
996	1	0.1%
994	2	0.2%
993	1	0.1%
988	5	0.5%
987	1	0.1%
985	2	0.2%
983	2	0.2%
979	4	0.4%

Rank
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	324
Distinct (%)	32.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	498.819

Minimum	1
Maximum	1000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	1
5-th percentile	50
Q1	250
median	500
Q3	750.25
95-th percentile	948.15
Maximum	1000
Range	999
Interquartile range (IQR)	500.25

Descriptive statistics

Standard deviation	288.8109971
Coefficient of variation (CV)	0.5789895675
Kurtosis	-1.200749808
Mean	498.819
Median Absolute Deviation (MAD)	250
Skewness	0.001245859611
Sum	498819
Variance	83411.79203
Monotonicity	Increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
760	13	1.3%
259	12	1.2%
455	9	0.9%
904	9	0.9%
386	9	0.9%
133	9	0.9%
700	8	0.8%
820	8	0.8%
312	8	0.8%
536	8	0.8%
Other values (314)	907	90.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	3	0.3%
4	2	0.2%
6	2	0.2%
8	2	0.2%
10	2	0.2%
12	1	0.1%
13	1	0.1%
14	2	0.2%
16	1	0.1%
17	2	0.2%

Value	Count	Frequency (%)
1000	1	0.1%
997	3	0.3%
996	1	0.1%
994	2	0.2%
993	1	0.1%
988	5	0.5%
987	1	0.1%
985	2	0.2%
983	2	0.2%
979	4	0.4%

Value
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	324
Distinct (%)	32.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.8817140937

Minimum	0.34
Maximum	1.71875
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	7.9 KiB

Quantile statistics

Minimum	0.34
5-th percentile	0.45655625
Q1	0.64953125
median	0.86
Q3	1.075
95-th percentile	1.40625
Maximum	1.71875
Range	1.37875
Interquartile range (IQR)	0.42546875

Descriptive statistics

Standard deviation	0.293524508
Coefficient of variation (CV)	0.3329021392
Kurtosis	-0.4524719248
Mean	0.8817140937
Median Absolute Deviation (MAD)	0.213125
Skewness	0.4299025249
Sum	881.7140937
Variance	0.08615663677
Monotonicity	Decreasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0.6375	13	1.3%
1.0625	12	1.2%
1.2375	9	0.9%
0.8925	9	0.9%
0.945625	9	0.9%
0.5	9	0.9%
0.825	8	0.8%
1.02	8	0.8%
0.6875	8	0.8%
0.584375	8	0.8%
Other values (314)	907	90.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.34	1	0.1%
0.357	3	0.3%
0.374	1	0.1%
0.3825	2	0.2%
0.391	1	0.1%
0.3995	5	0.5%
0.4	1	0.1%
0.408	2	0.2%
0.41	2	0.2%
0.4165	4	0.4%

Value	Count	Frequency (%)
1.71875	3	0.3%
1.703125	2	0.2%
1.671875	2	0.2%
1.65625	2	0.2%
1.640625	2	0.2%
1.625	1	0.1%
1.609375	1	0.1%
1.59375	2	0.2%
1.5625	1	0.1%
1.546875	2	0.2%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	first_name	last_name	gender	past_3_years_bike_related_purchases	DOB	job_title	job_industry_category	wealth_segment	deceased_indicator	owns_car	tenure	address	postcode	state	country	property_valuation	Unnamed: 16	Unnamed: 17	Unnamed: 18	Unnamed: 19	Unnamed: 20	Rank	Value
0	Chickie	Brister	Male	86	1957-07-12	General Manager	Manufacturing	Mass Customer	N	Yes	14	45 Shopko Center	4500	QLD	Australia	6	0.56	0.7000	0.875000	0.743750	1	1	1.718750
1	Morly	Genery	Male	69	1970-03-22	Structural Engineer	Property	Mass Customer	N	No	16	14 Mccormick Park	2113	NSW	Australia	11	0.89	0.8900	1.112500	0.945625	1	1	1.718750
2	Ardelis	Forrester	Female	10	1974-08-28	Senior Cost Accountant	Financial Services	Affluent Customer	N	No	10	5 Colorado Crossing	3505	VIC	Australia	5	1.01	1.0100	1.010000	1.010000	1	1	1.718750
3	Lucine	Stutt	Female	64	1979-01-28	Account Representative III	Manufacturing	Affluent Customer	N	Yes	5	207 Annamark Plaza	4814	QLD	Australia	1	0.87	1.0875	1.087500	1.087500	4	4	1.703125
4	Melinda	Hadlee	Female	34	1965-09-21	Financial Analyst	Financial Services	Affluent Customer	N	No	19	115 Montana Place	2093	NSW	Australia	9	0.52	0.5200	0.650000	0.650000	4	4	1.703125
5	Druci	Brandli	Female	39	1951-04-29	Assistant Media Planner	Entertainment	High Net Worth	N	Yes	22	89105 Pearson Terrace	4075	QLD	Australia	7	0.43	0.5375	0.537500	0.537500	6	6	1.671875
6	Rutledge	Hallt	Male	23	1976-10-06	Compensation Analyst	Financial Services	Mass Customer	N	No	8	7 Nevada Crossing	2620	NSW	Australia	7	0.40	0.4000	0.400000	0.340000	6	6	1.671875
7	Nancie	Vian	Female	74	1972-12-27	Human Resources Assistant II	Retail	Mass Customer	N	Yes	10	85 Carioca Point	4814	QLD	Australia	5	0.58	0.7250	0.725000	0.616250	8	8	1.656250
8	Duff	Karlowicz	Male	50	1972-04-28	Speech Pathologist	Manufacturing	Mass Customer	N	Yes	5	717 West Drive	2200	NSW	Australia	10	1.03	1.2875	1.609375	1.367969	8	8	1.656250
9	Barthel	Docket	Male	72	1985-08-02	Accounting Assistant IV	IT	Mass Customer	N	Yes	17	80 Scofield Junction	4151	QLD	Australia	5	0.84	1.0500	1.050000	0.892500	10	10	1.640625

Last rows

	first_name	last_name	gender	past_3_years_bike_related_purchases	DOB	job_title	job_industry_category	wealth_segment	deceased_indicator	owns_car	tenure	address	postcode	state	country	property_valuation	Unnamed: 16	Unnamed: 17	Unnamed: 18	Unnamed: 19	Unnamed: 20	Rank	Value
990	Jermaine	Bagshawe	Female	60	1954-05-14	Help Desk Operator	Property	Mass Customer	N	Yes	9	260 Briar Crest Drive	4209	QLD	Australia	6	0.51	0.6375	0.637500	0.541875	988	988	0.3995
991	Bryan	Jachtym	Male	59	1974-05-15	Automation Specialist I	Manufacturing	Mass Customer	N	Yes	15	56 Moland Crossing	3356	VIC	Australia	3	0.69	0.8625	0.862500	0.733125	988	988	0.3995
992	Renie	Laundon	Female	32	1973-12-18	Assistant Media Planner	Entertainment	Mass Customer	N	Yes	8	1 Shelley Pass	4118	QLD	Australia	3	1.08	1.3500	1.350000	1.147500	993	993	0.3910
993	Weidar	Etheridge	Male	38	1959-07-13	Compensation Analyst	Financial Services	Mass Customer	N	Yes	6	0535 Jay Point	2422	NSW	Australia	4	0.92	1.1500	1.150000	0.977500	994	994	0.3825
994	Datha	Fishburn	Female	15	1990-07-02	Office Assistant IV	Retail	Mass Customer	N	No	3	6 Caliangt Way	3079	VIC	Australia	12	0.77	0.7700	0.962500	0.818125	994	994	0.3825
995	Ferdinand	Romanetti	Male	60	1959-10-07	Paralegal	Financial Services	Affluent Customer	N	No	9	2 Sloan Way	2200	NSW	Australia	7	0.79	0.7900	0.790000	0.790000	996	996	0.3740
996	Burk	Wortley	Male	22	2001-10-17	Senior Sales Associate	Health	Mass Customer	N	No	6	04 Union Crossing	2196	NSW	Australia	10	0.76	0.7600	0.950000	0.807500	997	997	0.3570
997	Melloney	Temby	Female	17	1954-10-05	Budget/Accounting Analyst IV	Financial Services	Affluent Customer	N	Yes	15	33475 Fair Oaks Junction	4702	QLD	Australia	2	0.85	1.0625	1.062500	1.062500	997	997	0.3570
998	Dickie	Cubbini	Male	30	1952-12-17	Financial Advisor	Financial Services	Mass Customer	N	Yes	19	57666 Victoria Way	4215	QLD	Australia	2	1.09	1.3625	1.362500	1.158125	997	997	0.3570
999	Sylas	Duffill	Male	56	1955-10-02	Staff Accountant IV	Property	Mass Customer	N	Yes	14	21875 Grover Drive	2010	NSW	Australia	9	0.47	0.5875	0.734375	0.624219	1000	1000	0.3400

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Dash Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Space Separator

Other Punctuation

Dash Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Space Separator

Other Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Space Separator

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII